Segmenting Wine Market: California Red and White Wine Retail Prices in British Columbia
Bibliographic record
Abstract
Previous hedonic wine studies have employed conventional regression models to show the effects of objective and subjective attributes such as sensory characteristics, expert quality panel assessments, and regional reputation on wine prices. This paper employs a market segmentation approach based on price to show how lower, mid-priced and higher priced California red and white wines sold in British Columbia (BC) are influenced by objective attributes including geographical origin, grape variety, family brand names, alcohol content, and volume sales. Results show that red and white wine prices are segmented differently and the price segments for either wine type vary from those reported by earlier studies. Also, the effects of numerous attributes on wine prices vary significantly across wine types and price segments. The study findings show higher priced California Cabernet Sauvignon wines fetch a sizeable price premium compared to similar priced varietal wines like Merlot. Higher priced California white wines from Napa are discounted relative to wines labeled with a generic California appellation, whereas higher priced California red wines from Sonoma, Central Valley and Central Coast earn a price discount. Moreover, alcohol content is negatively related to higher priced California red wines, while positively associated with prices of mid-priced and higher priced California white wines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".